GraphAware®

RELEVANT SEARCH LEVERAGING KNOWLEDGE GRAPHS WITH NEO4J

Alessandro Negro Chief Scientist @ GraphAware

graphaware.com@graph_aware, @AlessandroNegro

‣ The rise of Knowledge Graphs

‣ Relevant Search

‣ Knowledge Graphs for e-Commerce

‣ Infrastructure

‣ Conclusions

OUTLINE

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“Knowledge graphs provide contextual windows

into master data domains and the links between

domains”

KNOWLEDGE GRAPH CONNECTING THE DOTS

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The Forrester Wave, Master Data Management

THE RISE OF KNOWLEDGE GRAPHS

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E-Commerce

‣ Many data sources

‣ Marketing strategies

‣ Business goals

‣ Category hierarchies

‣ Searches

Enterprise Networks

‣ Uncover new opportunities, hidden leads

Finance

‣ Textual corpora such as financial documents contain a wealth of knowledge

‣ Structured knowledge of entities and relationships

Medicine & Health

‣ Dynamic ontologies where data is categorized and organised around people, places, things and events

‣ Patterns in disease progression, causal relations involving disease and symptoms, new relationships previously unrecognised

Criminal Investigation & Intelligence

‣ Obfuscated information

‣ Traceability to sources of information

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THE RISE OF KNOWLEDGE GRAPHS

DATA SPARSITYPROBLEM

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Collaborative Filtering

‣ Cold Start

Content Based Recommendation

‣ Missing Data

‣ Wrong Data

Text Search

‣ User agnostic

‣ Relevant Search

KNOWLEDGE GRAPH: DATA CONVERGENCE

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RELEVANT SEARCH

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“Relevance is the practice of improving search

results for users by satisfying their information

needs in the context of a particular user

experience, while balancing how ranking

impacts business’s needs.”

RELEVANT SEARCH DIMENSIONS

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KNOWLEDGE GRAPHSTHE MODEL

Search architecture must be able to handle highly heterogenous data

Knowledge Graphs represent the information structure for relevant search

Graphs are the right representation for:

‣ Information Extraction

‣ Recommendation Engines

‣ Context Representation

‣ Rule Engine

Critical aspects and peculiarities:

‣ Defined and controlled set of searchable Items

‣ Multiple category hierarchies

‣ Marketing strategy

‣ User feedback and interactions

‣ Supplier information

‣ Business constraints

THE USE CASEE-COMMERCE

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→ Text search and catalog navigation as Sales People

KNOWLEDGE GRAPHFOR E-COMMERCE

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INFRASTRUCTUREA 10K-FOOT VIEW

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A graph centric approach

THE DATA FLOW

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‣ Async data ingestion

‣ Data Pipeline

‣ Single Neo4j Writer

‣ Microservice approach for isolation and scalability

‣ Event notification

‣ Multiple views exported into Elasticsearch

THE NEO4J ROLES

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‣ Single source of truth

‣ Cleansing

‣ Fast access to connected data

‣ Query

‣ Knowledge Graph store

‣ Merging External Data

‣ Existing Data Augmentation

Natural Language Processing

‣ Unsupervised Topic Identification

‣ Word2Vec

‣ Clustering (Label Propagation)

EXTERNALISE INTENSE PROCESSES

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Recommendation model building

‣ Content-Based

‣ Collaborative Filtering (internal and external)

Fast, Reliable and Easy-to-tune textual searches

‣ Multiple views for multiple scopes:

‣ Catalog Navigation and Search

‣ Faceting

‣ Product details page

‣ Product variants aggregation

‣ Autocomplete

‣ Suggestion

THE ELASTICSEARCH ROLES

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→ It is not used as a database

Any components of relevance-scoring calculation corresponding to a meaningful and measurable information

Two techniques to control relevancy:

‣ Signal Modeling

‣ Ranking Function

Note: balance precision and recall

Multiple sources

CRAFTINGSIGNALS

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→ Users as a new source of information

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Profile-based personalisation:

‣ Explicit: Users provide profile information

‣ Implicit: Profile created from user interactions

Behavioural-Based personalisation

‣ Focus on User-Item Interaction

‣ Make explicit the relationships among users and items

PERSONALISINGSEARCH

Tying personalisation back to search

‣ Query-time personalisation

‣ Index-time personalisation

→ Search for things, not for strings

CONCEPTSEARCH

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Basic Approaches:

‣ Concept field (Manual Tagging)

‣ Synonyms

Content Augmentation (ML based)

‣ Co-occurrence

‣ Latent Semantic Analysis

‣ Latent Dirichlet Allocation

‣ Word2Vec

COMBINED SEARCH APPROACHES

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Knowledge Graphs can

‣ store easy-to-query model

‣ gather data from multiple sources

‣ be easily extended

Search Engines can

‣ provide fast, reliable and easy-to-tune textual search

‣ provide features like faceting, autocomplete

CONCLUSION

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→ By combining them, it is possible to offer an unlimited set of services to the end users

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